Triple
T16229524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flashdance |
E393941
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Kyle T. Heffner |
E1023522
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kyle T. Heffner | Statement: [Flashdance, castMember, Kyle T. Heffner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyle T. Heffner Context triple: [Flashdance, castMember, Kyle T. Heffner]
-
A.
Kyle T. Heffner
chosen
Kyle T. Heffner is an American character actor known for supporting roles in films and television since the 1980s.
-
B.
Kyle B. O’Boyle
Kyle B. O’Boyle is a comedic UPS delivery man character in the Broadway musical "Legally Blonde," known for his flirtatious charm and memorable entrance.
-
C.
Kevin Burkhardt
Kevin Burkhardt is an American sportscaster best known as a play-by-play announcer and studio host for major MLB and NFL broadcasts on Fox.
-
D.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
-
E.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d2889688190ac04e4e9479cabf4 |
completed | April 17, 2026, 2:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025f740ec8190ab075c953e27cfba |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.